Instructions to use sam2ai/DeepSeek-R1-Distill-Qwen-7B-Multilingual-indic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sam2ai/DeepSeek-R1-Distill-Qwen-7B-Multilingual-indic with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="sam2ai/DeepSeek-R1-Distill-Qwen-7B-Multilingual-indic")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sam2ai/DeepSeek-R1-Distill-Qwen-7B-Multilingual-indic")
model = AutoModelForCausalLM.from_pretrained("sam2ai/DeepSeek-R1-Distill-Qwen-7B-Multilingual-indic", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sam2ai/DeepSeek-R1-Distill-Qwen-7B-Multilingual-indic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sam2ai/DeepSeek-R1-Distill-Qwen-7B-Multilingual-indic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sam2ai/DeepSeek-R1-Distill-Qwen-7B-Multilingual-indic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sam2ai/DeepSeek-R1-Distill-Qwen-7B-Multilingual-indic

SGLang

How to use sam2ai/DeepSeek-R1-Distill-Qwen-7B-Multilingual-indic with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "sam2ai/DeepSeek-R1-Distill-Qwen-7B-Multilingual-indic" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sam2ai/DeepSeek-R1-Distill-Qwen-7B-Multilingual-indic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "sam2ai/DeepSeek-R1-Distill-Qwen-7B-Multilingual-indic" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sam2ai/DeepSeek-R1-Distill-Qwen-7B-Multilingual-indic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sam2ai/DeepSeek-R1-Distill-Qwen-7B-Multilingual-indic with Docker Model Runner:
```
docker model run hf.co/sam2ai/DeepSeek-R1-Distill-Qwen-7B-Multilingual-indic
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

reasoning-multilingual-R1-Llama-70B-train

This model is a fine-tuned version of deepseek-ai/DeepSeek-R1-Distill-Qwen-7B on the reasoning-multilingual-R1-Llama-70B-train dataset. It achieves the following results on the evaluation set:

Loss: 0.5135

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 8
total_train_batch_size: 8
total_eval_batch_size: 8
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.01
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss
0.5539	0.3158	6	0.3874
0.5069	0.6316	12	0.4255
0.5634	0.9474	18	0.4700
0.7207	1.2632	24	0.5061
0.6525	1.5789	30	0.5270
0.7435	1.8947	36	0.5325
0.7488	2.2105	42	0.5383
0.5786	2.5263	48	0.5181
0.7325	2.8421	54	0.5146

Framework versions

Transformers 4.46.1
Pytorch 2.6.0.dev20241113+rocm6.2
Datasets 3.1.0
Tokenizers 0.20.3

Downloads last month: 4

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for sam2ai/DeepSeek-R1-Distill-Qwen-7B-Multilingual-indic

Base model

deepseek-ai/DeepSeek-R1-Distill-Qwen-7B

Finetuned

(313)

this model

Quantizations

1 model